Robust image registration is one of the problems at the core of research in computer vision and the emerging field of computational photography (CP). Various techniques for robust image registration exist and are based on methods such as aligning features within the image or applying dense pixel-to-pixel correspondence, among others.
A general approach in computational photography is to take multiple pictures of the same scene, each taken in a slightly different way, and then combine the images to produce one single photograph. Some of the goals in producing the final photograph include, for example, refocusing the image in post-processing or increasing the dynamic range of the photograph. In some cases, the goal of CP is to produce a single super-resolution image based on multiple lower resolution images.
Although super-resolution images can be obtained by increasing the resolution of a camera's image sensor, higher resolution sensors also give rise to higher fabrication costs and increased shot noise. Furthermore, increasing the sensor chip size to accommodate a larger number of pixels leads to increases in capacitance, which can reduce the data transfer rate. In addition, the application of pixel level registration among images, during the formation of a super-resolution image, can lead to artifacts such as blurring and the appearance of double features.